The era of smart interactive computer-based devices has dawned. There is a demand to increasingly develop common household items, such as computerized games and toys, smart gadgets and home appliances, personal digital assistants (PDA's), and mobile telephones, with new features, improved functionality, and built-in intelligence and/or intuition, and simpler user interfaces. The development of such products, however, has been hindered for a variety of reasons, including high cost, increased processing requirements, speed of response, and difficulty of use.
For example, in order to attain a share in the computer market today, computer game manufacturers must produce games that are challenging and maintain the interest of players over a significant period of time. If not, the games will be considered too easy, and consumers as a whole will opt not to purchase such games. In order to maintain a player's interest in single-player games (i.e., the player plays against the game program), manufacturers design different levels of difficulty into the game program. As the player learns the game, thus improving his or her skill level, he or she moves onto the next level. In this respect, the player learns the moves and strategy of the game program, but the game program does not learn the moves and strategy of the player, but rather increases its skill level in discrete step. Thus, most of today's commercial computer games cannot learn or, at the most, have rudimentary learning capacity. As a result, player's interest in the computer game will not be sustained, since, once mastered, the player will no longer be interested in the game. Even if the computer games do learn, the learning process is generally slow, ineffective, and not instantaneous, and does not have the ability to apply what has been learned.
Even if the player never attains the highest skill level, the ability of the game program to change difficulty levels does not dynamically match the game program's level of play with the game player's level of play, and thus, at any given time, the difficulty level of the game program is either too low or too high for the game player. As a result, the game player is not provided with a smooth transition from novice to expert status. As for multi-player computer games (i.e., players that play against each other), today's learning technologies are not well understood and are still in the conceptual stage. Again, the level of play amongst the multiple players are not matched with other, thereby making it difficult to sustain the players' level of interest in the game.
As for PDA's and mobile phones, their user applications, which are increasing at an exponential rate, cannot be simultaneously implemented due to the limitation in memory, processing, and display capacity. As for smart gadgets and home appliances, the expectations of both the consumers and product manufacturers that these new advanced products will be easier to use have not been met. In fact, the addition of more features in these devices has forced the consumer to read and understand an often-voluminous user manual to program the product. Most consumers find it is extremely hard to understand the product and its features, and instead use a minimal set of features, so that they do not have to endure the problem of programming the advanced features. Thus, instead of manufacturing a product that adapts to the consumers' needs, the consumers have adapted to a minimum set of features that they can understand.
Audio/video devices, such as home entertainment systems, provide an added dimension of problems. A home entertainment system, which typically comprises a television, stereo, audio and video recorders, digital videodisc player, cable or satellite box, and game console is commonly controlled by a single remote control or other similar device. Because individuals in a family typically have differing preferences, however, the settings of the home entertainment system must be continuously reset through the remote control or similar device to satisfy the preferences of the particular individual that is using the system at the time. Such preferences may include, e.g., sound level, color, choice of programs and content, etc. Even if only a single individual is using the system, the hundreds of television channels provided by satellite and cable television providers makes it difficult for such individual to recall and store all of his or her favorite channels in the remote control. Even if stored, the remote control cannot dynamically update the channels to fit the individual's ever changing preferences.
To a varying extent, current learning technologies, such as artificial intelligence, neural networks, and fuzzy logic, have attempted to solve the afore-described problems, but have been generally unsuccessful because they are either too costly, not adaptable to multiple users (e.g., in a family), not versatile enough, unreliable, exhibit a slow learning capability, require too much time and effort to design into a particular product, require increased memory, or cost too much to implement. In addition, learning automata theory, whereby a single unique optimum action is to be determined over time, has been applied to solve certain problems, e.g., economic problems, but have not been applied to improve the functionality of the afore-mentioned electronic devices. Rather, the sole function of the processing devices incorporating this learning automata theory is the determination of the optimum action.
There, thus, remains a need to develop an improved learning technology for processors.